In-class Exercise 5

This in-class exercise is a revision of what is covered in Hands-on Exercise 4, which revolves around spatial point patterns analysis.

Genice Goh true
09-13-2021

Installing and Loading Packages

packages = c('sf', 'spatstat', 'raster', 'maptools', 'tmap', 'tidyverse', 'plotly', 'ggthemes')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Importing Geospatial Data

mpsz_sf <- st_read(dsn="data/shapefile",
                   layer="MP14_SUBZONE_WEB_PL")
Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\Users\user\Desktop\SMU\Y4S1\IS415\geniceee\IS415_blog\_posts\2021-09-13-in-class-exercise-5\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

Importing Aspatial Data

read_rds() of readr package is used instead of readRDS() of base R is used. This is because the output of read_rds() is in tibble object.

childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")

Note: There are some data issues in childcare dataframe because Lat and Lng should be in numeric data type. The coordinate fields seem to be in decimal degrees and it is assumed that WGS84 referencing system is used.

Convert Aspatial Data into sf objects

CHAS_sf <- st_as_sf(CHAS, 
                    coords=c("X_COORDINATE", "Y_COORDINATE"),
                    crs=3414)

Note: st_as_sf() accepts coordinates in character data type

childcare_sf <- st_as_sf(childcare, 
                    coords=c("Lng", "Lat"),
                    crs=4326) %>%
  st_transform(crs=3414)

Plotting for Reviewing

tmap_mode('view')
tm_shape(childcare_sf) + 
  tm_dots(alpha=0.4,
          col="blue",
          size= 0.05) +
tm_shape(CHAS_sf) + 
  tm_dots(alpha=0.4,
          col="red",
          size= 0.05)

Geospatial Data Wrangling

Converting from sf to Spatial* dataframe

Using as_Spatial() of sf package

childcare <- as_Spatial(childcare_sf)
CHAS <- as_Spatial(CHAS_sf)
mpsz <- as_Spatial(mpsz_sf)

Converting Spatial* dataframe into Spatial* objects

Using as.SpatialPoint() / as.SpatialPolygon() of maptools package

childcare_sp <- as(childcare, "SpatialPoints")
CHAS_sp <- as(CHAS, "SpatialPoints")
mpsz_sp <- as(mpsz, "SpatialPolygons")

Converting Spatial* objects into ppp objects

Using as.ppp() function of maptools package

childcare_ppp <- as(childcare_sp, "ppp")
CHAS_ppp <- as(CHAS_sp, "ppp")

Remove Duplicate Points using Jitter

childcare_ppp_jit <- rjitter(childcare_ppp,
                             retry=TRUE,
                             nsim=1,
                             drop=TRUE)

any(duplicated(childcare_ppp_jit))
[1] FALSE
CHAS_ppp_jit <- rjitter(CHAS_ppp,
                             retry=TRUE,
                             nsim=1,
                             drop=TRUE)

any(duplicated(CHAS_ppp_jit))
[1] FALSE

Extract Punggol Planning Area

pg <- mpsz[mpsz@data$PLN_AREA_N=="PUNGGOL",]

Convert SpatialPolygonsDataFrame into Spatial Polygons object

pg_sp <- as(pg, "SpatialPolygons")

Convert SpatialPolygons into owin object

pg_owin <- as(pg_sp, "owin")

Extract Spatial Points within Owin

childcare_pg <- childcare_ppp_jit[pg_owin]
CHAS_pg <- CHAS_ppp_jit[pg_owin]
plot(childcare_pg)

L-function

L_childcare <- envelope(childcare_pg,
                        Lest,
                        nsim=99,
                        rank=1,
                        global=TRUE)
Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,  99.

Done.
L_CHAS <- envelope(CHAS_pg,
                        Lest,
                        nsim=99,
                        rank=1,
                        global=TRUE)
Generating 99 simulations of CSR  ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,  99.

Done.
title <- "Pairwise Distance: L function"

Lcsr_df <- as.data.frame(L_childcare)

colour=c("#0D657D","#ee770d","#D3D3D3")
csr_plot <- ggplot(Lcsr_df, aes(r, obs-r))+
  # plot observed value
  geom_line(colour=c("#4d4d4d"))+
  geom_line(aes(r,theo-r), colour="red", linetype = "dashed")+
  # plot simulation envelopes
  geom_ribbon(aes(ymin=lo-r,ymax=hi-r),alpha=0.1, colour=c("#91bfdb")) +
  xlab("Distance r (m)") +
  ylab("L(r)-r") +
  geom_rug(data=Lcsr_df[Lcsr_df$obs > Lcsr_df$hi,], sides="b", colour=colour[1])  +
  geom_rug(data=Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,], sides="b", colour=colour[2]) +
  geom_rug(data=Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,], sides="b", color=colour[3]) +
  theme_tufte()+
  ggtitle(title)

text1<-"Significant clustering"
text2<-"Significant segregation"
text3<-"Not significant clustering/segregation"

# the below conditional statement is required to ensure that the labels (text1/2/3) are assigned to the correct traces
if (nrow(Lcsr_df[Lcsr_df$obs > Lcsr_df$hi,])==0){ 
  if (nrow(Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,])==0){ 
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text3, traces = 4) %>%
      rangeslider() 
  }else if (nrow(Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,])==0){ 
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text2, traces = 4) %>%
      rangeslider() 
  }else {
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text2, traces = 4) %>%
      style(text = text3, traces = 5) %>%
      rangeslider() 
  }
} else if (nrow(Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,])==0){
  if (nrow(Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,])==0){
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text1, traces = 4) %>%
      rangeslider() 
  } else{
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text1, traces = 4) %>%
      style(text = text3, traces = 5) %>%
      rangeslider()
  }
} else{
  ggplotly(csr_plot, dynamicTicks=T) %>%
    style(text = text1, traces = 4) %>%
    style(text = text2, traces = 5) %>%
    style(text = text3, traces = 6) %>%
    rangeslider()
}
title <- "Pairwise Distance: L function"

Lcsr_df <- as.data.frame(L_CHAS)

colour=c("#0D657D","#ee770d","#D3D3D3")
csr_plot <- ggplot(Lcsr_df, aes(r, obs-r))+
  # plot observed value
  geom_line(colour=c("#4d4d4d"))+
  geom_line(aes(r,theo-r), colour="red", linetype = "dashed")+
  # plot simulation envelopes
  geom_ribbon(aes(ymin=lo-r,ymax=hi-r),alpha=0.1, colour=c("#91bfdb")) +
  xlab("Distance r (m)") +
  ylab("L(r)-r") +
  geom_rug(data=Lcsr_df[Lcsr_df$obs > Lcsr_df$hi,], sides="b", colour=colour[1])  +
  geom_rug(data=Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,], sides="b", colour=colour[2]) +
  geom_rug(data=Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,], sides="b", color=colour[3]) +
  theme_tufte()+
  ggtitle(title)

text1<-"Significant clustering"
text2<-"Significant segregation"
text3<-"Not significant clustering/segregation"

# the below conditional statement is required to ensure that the labels (text1/2/3) are assigned to the correct traces
if (nrow(Lcsr_df[Lcsr_df$obs > Lcsr_df$hi,])==0){ 
  if (nrow(Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,])==0){ 
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text3, traces = 4) %>%
      rangeslider() 
  }else if (nrow(Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,])==0){ 
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text2, traces = 4) %>%
      rangeslider() 
  }else {
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text2, traces = 4) %>%
      style(text = text3, traces = 5) %>%
      rangeslider() 
  }
} else if (nrow(Lcsr_df[Lcsr_df$obs < Lcsr_df$lo,])==0){
  if (nrow(Lcsr_df[Lcsr_df$obs >= Lcsr_df$lo & Lcsr_df$obs <= Lcsr_df$hi,])==0){
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text1, traces = 4) %>%
      rangeslider() 
  } else{
    ggplotly(csr_plot, dynamicTicks=T) %>%
      style(text = text1, traces = 4) %>%
      style(text = text3, traces = 5) %>%
      rangeslider()
  }
} else{
  ggplotly(csr_plot, dynamicTicks=T) %>%
    style(text = text1, traces = 4) %>%
    style(text = text2, traces = 5) %>%
    style(text = text3, traces = 6) %>%
    rangeslider()
}